Community Discovery Library
CDlib - Community Discovery Library
CDlib is a meta-library for community discovery in complex networks: it implements algorithms, clustering fitness functions as well as visualization facilities.
CDlib is designed around the
networkx python library: however, when needed, it takes care to automatically convert (from and to)
igraph object so to provide an abstraction on specific algorithm implementations to the final user.
CDlib provides a standardized input/output facilities for several Community Discovery algorithms: whenever possible, to guarantee literature coherent results, implementations of CD algorithms are inherited from their original projects (acknowledged on the documentation).
If you use
CDlib as support to your research consider citing:
G. Rossetti, L. Milli, R. Cazabet. CDlib: a Python Library to Extract, Compare and Evaluate Communities from Complex Networks. Applied Network Science Journal. 2019. DOI:10.1007/s41109-019-0165-9
Tutorial and Online Environments
Check out the official tutorial to get started!
If you would like to test
CDlib functionalities without installing anything on your machine consider using the preconfigured Jupyter Hub instances offered by SoBigData++.
CDlib requires python>=3.8.
To install the latest version of our library just download (or clone) the current project, open a terminal and run the following commands:
pip install -r requirements.txt pip install -r requirements_optional.txt # (Optional) this might not work in Windows systems due to C-based dependencies. pip install .
Alternatively use pip
pip install cdlib
conda config --add channels giuliorossetti conda config --add channels conda-forge conda install cdlib
Optional Dependencies (pip package)
CDlib relies on a few packages calling C code that can be cumbersome to install on Windows machines: to address such issue, the default installation does not try to install set up such requirements.
Such a choice has been made to allow (even) non *unix user to install the library and get access to its core functionalities.
To integrate the standard installation with you can either:
- (Windows) manually install the optional packages (versions details are specified in
requirements_optional.txt) following the original projects guidelines, or
- (Linux/OSX) run the command:
pip install cdlib[C]
Such caveat will install everything that can be easily automated under Linux/OSX.
The only optional dependency that will remain unsatisfied following the previous procedures will be
graph-tool (used to add SBM models).
If you need it up and running, refer to the official documentation and install the conda-forge version of the package.
Since its 2.1.0 release
ASLPAw relies on
gmpy2 whose installation through pip is not easy to automatize due to some C dependencies.
To address such issue test the following recipe:
conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0
In case this does not solve the issue, please refer to the official
gmpy2 installation instructions.
Optional Dependencies (Conda package)
CDlib relies on a few packages not available through conda: to install it please use pip:
pip install pycombo pip install GraphRicciCurvature conda install gmpy2 pip install shuffle_graph>=2.1.0 similarity-index-of-label-graph>=2.0.1 ASLPAw>=2.1.0
In case ASLPAw installation fails, please refer to the official
gmpy2 installation instructions.
Collaborate with us!
CDlib is an active project, any contribution is welcome!
If you like to include your model in CDlib feel free to fork the project, open an issue and contact us.
How to contribute to this project?
We are a welcoming community... just follow the Code of Conduct.
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